Survey on Detection of Glaucoma in Fundus Image by Segmentation and Classification

Abstract

Reliable glaucoma detection in digital fundus images is still an open issue in biomedical image processing. The detection of glaucoma in retinal fundus image is essential for preventing from the vision loss. Glaucoma is an irretrievable chronic eye disease which leads to blindness that caused due to the damage of optic nerves. The time of glaucoma detection is very important to be slowed down by treatment whereas glaucoma cannot be cured. Particularly there is no effective method for detection of glaucoma in current status. Nowadays many studies have shown that the detection or screening of glaucoma in 2D retinal fundus image. This paper addresses the survey on various methods of segmentation and classification technique to detect the glaucoma from the retinal images based on the Cup to Disc Ratio (CDR) evaluation of preprocessed image. This survey paper presents an image processing technique for segmentation of optic disc and cup as well as diagnosis of glaucoma using obtained the features from the image based on the study of adaptive thresholding technique and SVM classification technique compared to remaining or existing algorithms.